Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.